Anti-Inflammatory Biologics and Anti-Tumoral Immune Therapies-Associated Colitis: A Focused Review of Literature
Bibliographic record
Abstract
An increasing number of drugs including monoclonal antibodies and small molecules, either anti-inflammatory or immunity-enhancing, have been developed to treat human diseases and the number of medications in these classes is likely to expand in the future. The two most commonly used categories of such therapies are the anti-inflammatory group (anti- tumor necrosis factor (TNF) α, anti-interleukins/interleukin receptors, and anti-integrin bodies) and the anti-tumoral agents (immune checkpoint inhibitors, anti-CD20, and anti-endothelial growth factor). Although the anti-inflammatory biologics have brought about a revolutionary effect in the management of a variety of autoimmune disorders including rheumatologic diseases, inflammatory bowel disease, and inflammatory dermatological diseases, their ability to induce colitis in patients without a prior history of colitis or exacerbate quiescent colitis has been increasingly and unexpectedly recognized. While the use of immune-augmenting monoclonal antibody therapies results in a significant survival benefit in a subset of patients with malignancies, these monoclonal antibodies also have the ability to cause colitis through an apparent autoimmune mechanism. Colitis associated with these medications may demonstrate multiple histologic patterns including increased apoptosis (graft versus host disease (GVHD)-like), autoimmune enteropathy pattern, acute colitis pattern, ischemic colitis, inflammatory bowel disease pattern, either ulcerative colitis-like, Crohn's disease-like, or fulminant colitis-like. In addition, anti-inflammatory biologics are known to cause or reactivate latent infections such as tuberculosis and increase the risk for malignancies including high-grade lymphomas as well as indolent lymphoproliferative disorders. Thus, the differential diagnosis for colitis in patients receiving therapeutic anti-inflammatory biologics or anti-tumoral agents can be broad. Optimal diagnosis and treatment requires a multidisciplinary approach. This review aims to provide an overview of the literature on the clinical features, histology, and treatment of these newly recognized anti-inflammatory biologic and anti-tumoral immune therapy-induced colitises and hopes this outlines will raise the vigilance of all clinicians of these entities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".